Pipeline Frameworks — LangChain, LlamaIndex and when you actually need them
LangChain, LlamaIndex, LangGraph and Haystack compared. What they're built for, when rolling your own pays off — and the criticisms worth taking seriously.
Redaktion
Pipeline-Frameworks
in KI-Werkzeuge
Frameworks zum Verketten von LLM-Aufrufen und RAG-Pipelines.
Glossary
DSPy Pipeline-Frameworks
DSPy is an open-source Python framework from the Stanford NLP group that lets you program LLM applications instead of hand-crafting prompts. Tasks are described through declarative signatures and modules; optimizers automatically derive effective prompts and examples.
Flowise Pipeline-Frameworks
Flowise is an open-source low-code tool for building LLM applications and AI agents visually via drag-and-drop. It is built on the LangChain ecosystem and connects models, data sources and tools as nodes.
Haystack Pipeline-Frameworks
Haystack is an open-source AI orchestration framework from deepset for building production-ready LLM applications. Modular components — retrievers, generators, routers, tools — are assembled into explicit pipelines for RAG, agents and semantic search.
LangChain Pipeline-Frameworks
Open-source framework that chains LLMs with data sources, tools and memory into applications — arguably the best-known pipeline library for AI apps.
LlamaIndex Pipeline-Frameworks
Data framework for LLM applications, specialized in indexing, enriching and retrieving private data — at the core of many RAG architectures.